相关论文: Machine learning delta-T noise for temperature bia…
In this paper, we carry out a theoretical analysis of the zero-frequency and finite-frequency shot noise in electron tunneling through a two-level interacting system connected to two leads, when a coherent coupling between the two levels is…
Data-driven modeling and control of temperature dynamics in mechatronics systems and industrial processes are challenging control engineering problems. This is mainly because the temperature dynamics is inherently infinite-dimensional,…
The predictive accuracy of density functional theory (DFT) for alloy formation enthalpies is often limited by intrinsic energy resolution errors, particularly in ternary phase stability calculations. In this work, we present a machine…
Urban heat islands, defined as specific zones exhibiting substantially higher temperatures than their immediate environs, pose significant threats to environmental sustainability and public health. This study introduces a novel…
We present a machine-learning approach toward predicting spectroscopic constants based on atomic properties. After collecting spectroscopic information on diatomics and generating an extensive database, we employ Gaussian process regression…
We investigate the transverse momentum spectra of identified particles at 7 TeV and 13 TeV in pp collisions in the framework of the blast wave model with Tsallis statistics (TBW). Based on experimental data by ALICE Collaboration, we…
Since model bias and associated initialization shock are serious shortcomings that reduce prediction skills in state-of-the-art decadal climate prediction efforts, we pursue a complementary machine-learning-based approach to climate…
Temperature is a fundamental regulator of chemical and biochemical kinetics, yet capturing nonlinear thermal effects directly from experimental data remains a major challenge due to limited throughput and model flexibility. Recent advances…
The accurate determination of electronic temperatures in metallic nanostructures is essential for many technological applications, like plasmon-enhanced catalysis or lithographic nanofabrication procedures. In this Letter we demonstrate…
With the Thomson scattering (TS) system in KSTAR, temporal evolution of electron temperature ($T_e$) is estimated using a weighted look-up table method with fast sampling ($1.25$ or $2.5$ GS/s) digitizers during the 2014 KSTAR campaign.…
There currently exist no quantitative methods to determine the appropriate conditions for solid-state synthesis. This not only hinders the experimental realization of novel materials but also complicates the interpretation and understanding…
We present a method for the measurement of a temperature differential across a single quantum dot that has transmission resonances that are separated in energy by much more than the thermal energy. We determine numerically that the method…
Various techniques can be employed to determine the temperature of magnetic transformation, whether it be the Curie or Neel temperature. The standard procedure typically involves creating alloys with defined compositions and performing…
Decadal temperature prediction provides crucial information for quantifying the expected effects of future climate changes and thus informs strategic planning and decision-making in various domains. However, such long-term predictions are…
Understanding when the noise in stochastic gradient descent (SGD) affects generalization of deep neural networks remains a challenge, complicated by the fact that networks can operate in distinct training regimes. Here we study how the…
Voltage-gated ion channels are essential for propagating signals in neurons. Each channel senses the local membrane potential created by nearby ions. Fluctuations in these ions introduce two fundamental noise sources: (i) shot noise, from…
We discuss inherent thermometry in a Superconductor - Normal metal - Superconductor tunnel junction. In this configuration, the energy selectivity of single-particle tunneling can provide a significant electron cooling, depending on the…
Nuclear power plant operators face significant challenges due to unpredictable deviations between offline and online thermal limits, a phenomenon known as thermal limit bias, which leads to conservative design margins, increased fuel costs,…
Since the internal temperature is less accessible than surface temperature, there is an urgent need to develop accurate and real-time estimation algorithms for better thermal management and safety. This work presents a novel framework for…
Most of the work on chatter detection is based on laboratory machining tests, thus without the constraints of noise, the variety of situations to be managed in the industry, and the uncertainties on the parameters (sensor position, tool…